Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks

Barchid, Sami; Mennesson, José; Djeraba, Chabane

doi:10.1109/cbmi50038.2021.9461875

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…The recall rate is the ratio of one target box to all target boxes that the model predicts correctly. In general, the recall rate and accuracy rate are difficult to be at a high level, so the parameters are extracted to measure the network performance [15] .…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Research on staircase image detection technology

gao,

Xue

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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As a typical environmental target, the staircase area needs to have the function of detecting and recognizing stairs, whether it is a robot system that can climb stairs independently or a software system that can remind visually impaired people to pay attention to obstacles. A staircase region detection algorithm with dual attention mechanism and bidirectional cross scale feature fusion is proposed to address the issue of false detections and missed detections in current staircase images due to their similar shapes to areas such as tiles, tables, and chairs in reality. Firstly, a dual attention mechanism was introduced into the backbone network of Yolov5s to extract semantic dependencies between different dimensions, eliminate indirect correspondence between channels and weights, and improve detection accuracy by focusing on differences in similar samples. Secondly, a weighted bidirectional feature pyramid network structure is adopted in the feature pyramid to achieve efficient bidirectional cross scale connections and weighted feature fusion, enhancing the network's feature extraction ability. The experimental results show that compared with the original algorithm, the improved algorithm has mAP@0.5 Improved by 1.3%, with higher detection accuracy. In addition, we also conducted experiments on staircase data collected under different lighting conditions to verify the effectiveness of the algorithm.

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Research on staircase image detection technology

gao,

Xue

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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“…With the development of low-cost depth sensors, there are increasingly more RGB-D-based deep learning methods in computer vision. However, there is no established methodology to perfectly fuse these two modalities inside a CNN [ 23 ]. Some common fusion methods are as follows.…”

Section: Related Workmentioning

confidence: 99%

RGB-D-Based Stair Detection and Estimation Using Deep Learning

Wang

Pei

Qiu

et al. 2023

Sensors

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Stairs are common vertical traffic structures in buildings, and stair detection tasks are important in environmental perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a stair detection network with red-green-blue (RGB) and depth inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB feature maps and the depth feature maps and fuse the features effectively in different scenes. In addition, we propose several postprocessing algorithms, including a stair line clustering algorithm and a coordinate transformation algorithm, to obtain the stair geometric parameters. Experiments show that our method has better performance than existing the state-of-the-art deep learning method, and the accuracy, recall, and runtime are improved by 5.64%, 7.97%, and 3.81 ms, respectively. The improved indexes show the effectiveness of the multimodal inputs and the selective module. The estimation values of stair geometric parameters have root mean square errors within 15 mm when ascending stairs and 25 mm when descending stairs. Our method also has extremely fast detection speed, which can meet the requirements of most real-time applications.

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“…The taxonomy we adopt in this work is closer to the one proposed by Barchid et al [51]. The authors classify RGB-D methods according to how depth data is used in the network, into: depth as input, depth as operation and depth as prediction -Fig.…”

Section: Rgb-d Semantic Segmentationmentioning

confidence: 99%

A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design

Manfio¹,

Osório²

2023

Preprint

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Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Networks, have boosted semantic segmentation to a new level in terms of performance and generalization capabilities. However, designing Deep Semantic Segmentation models is a complex task, as it may involve application-dependent aspects. Particularly, when considering autonomous driving applications, the robustness-efficiency tradeoff, as well as intrinsic limitations -computational/memory bounds and data-scarcity -and constraints -real-time inference -should be taken into consideration. In this respect, the use of additional data modalities, such as depth perception for reasoning on the geometry of a scene, and temporal cues from videos to explore redundancy and consistency, are promising directions yet not explored to their full potential in the literature. In this paper, we conduct a survey on the most relevant and recent advances in Deep Semantic Segmentation in the context of vision for autonomous vehicles, from three different perspectives: efficiency-oriented model development for real-time operation, RGB-Depth data integration (RGB-D semantic segmentation), and the use of temporal information from videos in temporalaware models. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective, so that the reader can not only get started, but also be up to date in respect to recent advances in this exciting and challenging research field.

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Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks

Cited by 10 publications

References 26 publications

Research on staircase image detection technology

Research on staircase image detection technology

RGB-D-Based Stair Detection and Estimation Using Deep Learning

A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design

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